Intelligence
AI integration in Orlando, Florida
AI integration is worth doing where a task is repetitive, language-heavy and currently done by a person reading something. Answering the same policy question, pulling numbers out of invoices, sorting an inbox, drafting the first version of a reply.
It is not worth doing as a feature you can announce. We will tell you when a rule, a filter or a better form beats a model, because that is usually cheaper and always more predictable.
- Assistants
- Document AI
- Semantic search
Good candidates for AI
- Staff answer the same questions from documents nobody wants to read.
- Invoices, forms or contracts are re-keyed into a system by hand.
- Search in your own system fails unless you know the exact wording.
- Support messages need sorting and routing before anyone can act.
- A first draft would save real time, even when a person still reviews it.
Scope
What we build with AI
Every one of these is grounded in your data, with a citation or a source the user can check. An assistant that cannot show its source is a liability.
- 01
Assistants over your own content
A question-answering assistant grounded in your manuals, policies and records, answering with references, and saying it does not know rather than inventing.
- 02
Document processing
Invoices, forms, contracts and reports read automatically into structured data, with a confidence score and a human review step for anything uncertain.
- 03
Semantic search
Search that understands intent rather than keywords, so people find the record they mean instead of the one they typed.
- 04
Classification and routing
Incoming messages, tickets or leads categorized and routed automatically, with the rules visible and adjustable by your team.
- 05
Drafting with a human in the loop
First drafts of quotes, replies and summaries prepared for a person to approve. Speed without handing judgement to a model.
- 06
Guardrails, cost control and privacy
Rate limits, spend monitoring, prompt-injection defenses and a written answer to where your data goes. We treat an AI feature as an attack surface, because it is one.
We start with a narrow evaluation on your real documents. If the accuracy is not good enough to trust, you find out in week one instead of after the build.
Anything that reaches a customer keeps a human in the loop until the numbers justify removing them. Anything that touches money keeps one permanently.
We are model-agnostic and route through a gateway, so you are not tied to one vendor’s pricing or availability. Cost per task is monitored from the first day it is live.
Where this creates value
Teams drowning in documents and repetitive written work: clinical admin, claims, contracts, compliance, customer support and quoting.
- AI for healthcarePatient data lives in paper, spreadsheets and three systems nobody trusts.Read more
- AI for professional servicesBillable hours get reconstructed from memory at the end of the month.Read more
- AI for startupsThe prototype that won the first customers cannot carry the next hundred.Read more
FAQ
Questions we get before a project starts
Will our data be used to train a model?
Not with the configurations we deploy. We use providers and settings with zero data retention for training, and we document exactly what leaves your infrastructure and where it goes.
What if the AI gets it wrong?
We design for that. Answers cite their source, uncertain results are routed to a person, and anything financial or clinical keeps human approval. A system that cannot be wrong safely should not be automated.
How much does it cost to run?
There is a build cost and a usage cost. We measure cost per task during the evaluation, so you know the monthly number before launch rather than discovering it on an invoice.
Do we need AI at all?
Frequently not. If the problem is a badly designed form, a missing integration or an unclear process, fixing that is cheaper and more reliable. We would rather sell you business automation that works than an AI feature that impresses.
Have a task built on reading and re-typing?
Send us the process. We will test it on your real data before anyone commits to a build.